By |Published On: January 3rd, 2013|Categories: Research Insights, Momentum Investing Research|

Generalized Momentum and Flexible Asset Allocation (FAA) An Heuristic Approach

  • Wouter J. Keller and Hugo S.van Putten
  • A recent version of the paper can be found here.
  • Note: CXO Advisory has a recent post on this, but I was 90% done with this post so I’m posting anyway. CXO has a more detailed analysis for subscribers to their site.


In this paper we extend the timeseries momentum (or trendfollowing) model towards a generalized momentum model, called Flexible Asset Allocation (FAA). This is done by adding new momentum factors to the traditional momentum factor R based on the relative returns among assets. These new factors are called Absolute momentum (A), Volatility momentum (V) and Correlation momentum (C). Each asset is ranked on each of the four factors R, A, V and C. By using a linearised representation of a loss function representing risk/return, we are able to arrive at simple closed form solutions for our flexible asset allocation strategy based on these four factors. We demonstrate the generalized momentum model by using a 7 asset portfolio model, which we backtest from 1998-2012, both in- and out-of-sample.

Data Sources:


Strategy Summary:

  • Test a few different signals for market and asset class timing.  The authors then combine these signals.  They are using 7 asset classes described here:
    • Our example universe consists of 7 index funds (so U=7), i.e. 3 for global stocks (VTSMX, FDIVX, VEIEX) covering US, EAFE and EM regions, 2 for US bonds (VFISX, VBMFX) and a commodity and REIT index fund (QRAAX, VGSIX). Users only interested in recent years can use the corresponding ETFs (eg. VTI, VEA, VWO, SHY, BND, GSG, and VNQ) which follow the same indices as our index funds.
  • They use a 4 month lookback for prices in the paper.
  • Each month, rank all 7 based on relative momentum (higher is better), volatility (lower is better), and correlations (lower is better).  So each of the seven assets has a rank from 1-7 for the 3 factors.
  • Then rank using this equation:
    • Li = wR * rank(ri) + wV * rank(vi) + wC * rank (ci)
    • Authors arbitrarilly set wR=1, wV=0.5, and wC=0.5.
  • This new variable “Li” now ranks all the assets on relative momentum, volatility, and correlations.  Pick the top 3 assets each month and equal weight these.
    • Last, for each of the top 3 assets chosen above, check their absolute momentum – if this is negative, just go into cash.
  • Make money!

Strategy Commentary:

  • Simple way to combine absolute momentum, relative momentum, volatility, and correlations.
  • Paper also shows better returns when not equal weighting the top 3 assets, but this is more complicated.
  • Cool paper…but one gripe…

Translation of the very obtuse abstract:

We mix momentum, risk parity, and correlation factors–factors all known to work in sample for tactical asset allocation models–and compile them into a model that tells us what we already know: these factors work historically. We forgot to include a test of our model against Meb Faber’s ridiculously easy long-term moving average rule as a benchmark comparison (instead opting to include the buy&hold benchmark, which sucks), because that would make all our complicated models seem worthless.

One paper you might want to explore if this sort of stuff turns you on is Gary Antonacci’s piece:

Print Friendly, PDF & Email

About the Author: Wesley Gray, PhD

Wesley Gray, PhD
After serving as a Captain in the United States Marine Corps, Dr. Gray earned an MBA and a PhD in finance from the University of Chicago where he studied under Nobel Prize Winner Eugene Fama. Next, Wes took an academic job in his wife’s hometown of Philadelphia and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management firm dedicated to an impact mission of empowering investors through education. He is a contributor to multiple industry publications and regularly speaks to professional investor groups across the country. Wes has published multiple academic papers and four books, including Embedded (Naval Institute Press, 2009), Quantitative Value (Wiley, 2012), DIY Financial Advisor (Wiley, 2015), and Quantitative Momentum (Wiley, 2016). Dr. Gray currently resides in Palmas Del Mar Puerto Rico with his wife and three children. He recently finished the Leadville 100 ultramarathon race and promises to make better life decisions in the future.

Important Disclosures

For informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice. Certain information is deemed to be reliable, but its accuracy and completeness cannot be guaranteed. Third party information may become outdated or otherwise superseded without notice.  Neither the Securities and Exchange Commission (SEC) nor any other federal or state agency has approved, determined the accuracy, or confirmed the adequacy of this article.

The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).

Join thousands of other readers and subscribe to our blog.

Print Friendly, PDF & Email